The FLOW mechanism is originally proposed for sequence-model-based readers. It also inspires the development of several BERT-based CMC readers that use the integrated context vectors of dialog history for answer prediction.
[1]} use BERT to encode for each question and answer a contextual representation (i.e., a sequence of vectors) conditioned on the passage. As illustrated in Figure REF , given a history consisting of \(K\)  QA pairs and the input question, there are \(2K+1\)  resultant contextual representations. Then, a bidirectional RNN model runs over the concatenation of the these vector sequences for answer span prediction.
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